I was pulled into this article when I read “a gigantic shift in computing is about to dawn upon us.” The gist – the US and China are investing heavily in designing high-powered AI chips to handle the linear algebra computations used in AI and this represents a fundamental change in how we build software and hardware. There are some examples of how these chips are being used – for face recognition, map street view, chatbots, and self-driving cars to name a few. Of course data is needed to train AI algorithms and that has come from private companies. Top AI talent will also come from those companies too ( I wish I paid attention to algebra in school). Worth a read is the link to China’s strategy and agenda for “intelligentization.” All that said, in my opinion anyway, the most interesting aspect of all of this is going to be ethics: robots rights, threats to privacy, discrimination, moral considerations, etc.
O’Reilly On Our Radar | The artificial intelligence computing stack | December 20, 2017
Here’s a great (kinda wonky) article by Ben Lorica on O’Reilly’s “On Our Radar.” Ben shares slides from a recent presentation, offering an overview of the state of adoption of AI and suggestions to companies interested in implementing AI technologies. Ben also shares a sketch of a typical tech stack for intelligent applications.
Notable is a recent survey of 3,000 executives, managers, and analysts conducted by MIT Sloan Management Review that suggests low adoption (54% have not started adopting AI technologies). The author recommends the following:
Educate yourself on the current state of tools and technologies, identifying use cases in your domain or industry starting with small pilot projects.
Maybe start with this article.
O’Reilly On Our Radar | The state of AI adoption | December 18, 2017
The last paragraph of this article by Gigaom offers good advice about AI: look for solution providers that are using AI to help improve workflows based on workers’ behaviors. In my experience, this often means looking beyond solutions that are ready-made for learning.
Gigaom also identifies four “tectonic shifts” taking place that are driven by the need to provide “greater personalization and efficiency in how we use technology.” The first shift listed is the changing behavior and expectations of Millennials and Gen Z employees. Blah, blah, blah… enough generational stuff! (It’s always been a pet peeve.) Newsflash: The desire for a “consumer-level technologies” that allow for a “quick, efficient, and intuitive” experience is something most workers expect (note I didn’t say employees) regardless of age. Even crotchety old Gen X’ers like me want an online experience that helps us accomplish our work quickly, efficiently, and more intuitively.
The second shift listed is digital transformation, defined in the article as a modernization of business activities, processes and models to become completely digitized. Again, not really a tectonic shift as much as evolution – although a very difficult shift to make.
The third shift is about the trend toward technology procurement and a change in how decisions are made – at the unit or team-level vs. the C-level. I see this more as learning becomes more about the work.
The fourth shift is cloud-based technology training challenges associated with rapid change and how AI may help solve this challenge. For me that means thinking about training differently. AI will be big deal in 2018.
Gigaom | How Artificial Intelligence Will Personalize How We Work | December 14, 2017